As processing power and memory capacity increase, a need arises for control mechanisms that permit a machine to fully exploit system capabilities. In the case of printers, photocopiers, and the like, conventional control mechanisms are limited in the manner in which they process information, allocate resources, perform jobs or tasks, etc. When designing a control system for a machine, it is desirable to optimize resource allocation and utilization in order to reduce cost and increase throughput. For instance, a resource that is capable of multiple concurrent allocations may be employed to provide improved performance in a machine, such as a printer or photocopier. Traditionally, a single resource could be allocated to a single component or for a single task at a given time. However, in the case of multi-capacity resources (e.g., resources capable of multiple allocations at a given time), control mechanisms to date have failed to provide planning and scheduling mechanisms that optimize resource capacity.
For instance, some attempts at control optimization for multi-function, multi-resource parallel-operation systems have employed manually encoded rules, which to date have not been concluded to be optimal or complete. Other approaches, such as adding component descriptions to responsibilities associated with a planning engine may be functional but may lead to combinatorial challenges in planner performance. However, there exists an unmet need in the art for systems and/or methodologies that facilitate optimizing multi-capacity resource utilization while minimizing computational overhead to improve throughput and reduce costs associated with machine control.